Bent tilted fiber Bragg gratings for temperature-independent vibration sensing in vehicles
Bibliographic record
Abstract
Fiber grating based techniques have proven themselves as low cost, small size and low weight solutions for sensing strain and/or temperature in various applications, including structural health monitoring of aircrafts, ships and other man-made structures. However, normal fiber gratings are sensitive to both strain and temperature in a manner that is impossible to distinguish from the sensor response. Methods devised to circumvent this problem rely on combinations of gratings with different sensitivity to these two perturbations. Simultaneous measurements on two gratings then provide the necessary information to decode strain and temperature values but this requires special grating configurations and packaging to maximize the differential sensitivity. We will present experimental results of an alternative approach where we use a single very weakly tilted fiber Bragg grating (TFBG), to achieve the same effect. The grating couples light from the fundamental mode guided in the core to a large number of cladding modes, depending on the wavelength of interrogation. We propose and demonstrate a novel configuration in which many high order cladding mode resonances are removed by bonding the TFBG in a pre-bent state on a metal plate. After bonding, only a few low order mode resonances are left and occupy less than 5 nm of bandwidth (thereby allowing multiplexing). These resonances all have the same temperature sensitivity but very different behavior when the plate vibrates, bends or stretches statically. Differential measurements of the resonance power levels and shifts then provide valuable information about the mechanical state of the sensor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".